1,720,973 research outputs found
How do fintech firms address financial inclusion?
Full text linkFinancial technology (fintech) firms are transforming the financial sector with new innovative services. Indeed, fintech firms have established themselves as viable addition to the traditional financial structure and are potentially playing an important role in addressing financial inclusion. However, hitherto there is limited understanding of how fintech firms address financial inclusion. In this paper, we present a preliminary phase of research and analysis on a case study of fintech start-ups in Ghana. We address the research question: “how do fintech firms address financial inclusion?” Informed through activity theory, our preliminary findings show that fintech firms act as innovators and aggregators, leverage existing infrastructure of incumbents and deploy cooperative strategies comprising elements of competition and cooperation to address financial inclusion. Based on the findings, this study develops a fintech driven financial inclusion model that explains how fintech firms work towards addressing financial inclusion
Trust in AI systems for project and risk management: evaluating the role of transparency, reputation, technical competence, and reliability
No full textMachine learning algorithms are often perceived as opaque, undermining user trust in AI systems. Explainable AI (XAI) seeks to mitigate this by enhancing transparency through clear explanations of algorithmic predictions. Furthermore, the reliability of these systems can be improved by integrating predictions from multiple algorithms. This study developed a hybrid project prediction system that merges XAI with several machine learning algorithms, thus fostering trust among project professionals and decision-makers. The theoretical model, grounded in literature on technology adoption, inter-organisational relationships, and XAI, examines four key trust factors: transparency, reputation, technical competence, and reliability. Employing a survey experiment and structural equation modelling, the research provides a nuanced understanding of how these factors influence trust in AI applications within project and risk management, contributing significantly to both academic literature and practical implementations
Applied algorithmic machine learning for intelligent project prediction: towards an AI framework of project success
No full textA growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets, in order to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. For example, prior studies have used machine learning models to calculate and perform predictions. Artificial neural networks are the most frequently used machine learning model with support vector machine, and genetic algorithm and decision trees are sometimes used in several related studies. Furthermore, most machine learning algorithms used in prior studies generally assume that inputs and outputs are independent of each other, which suggests that a project's success is expected to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice projects are often interrelated across several different dimensions, for example through distributed overlapping teams. An ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron has been found to be one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice
FinTech ecosystem practices shaping financial inclusion: the case of mobile money in Ghana
No full textFinancial technology (FinTech) is widely recognised as important in addressing financial inclusion. However, limited research theorises how new entrants and incumbents work together in FinTech ecosystems to shape financial inclusion. We undertake a theory-generating case study with multilevel interacting organisations in Ghana, where, like many other African countries, the growth in FinTech has led to new opportunities for financial inclusion. We conceptualise three practices, as building blocks at the ecosystem level, through which incumbents and new entrants shape financial inclusion: (1) innovative and collaborative practices, (2) protectionist and equitable practices, and (3) legitimising and sustaining practices. We articulate a theoretical model that explains how the practices shape financial inclusion and propose three theoretical propositions of how financial inclusion in developing countries is being scaled and shaped in terms of actors, relationships, and practices
Digital Bricolage and Its Limits: How Micro-Enterprises Undertake Digitalization in Resource-Constrained Environments
No full textDeparting from traditional theory on digitalization, we argue that smaller enterprises in resource-constrained environments may take a different route to digitalization that is less strategic and more emergent; less engineered, and more oriented to the situation at hand; less driven by sophisticated technology and more likely to embody frugality. We ask, “how do micro-enterprises ‘make do’ their digitalization in resource-constrained environments?”, addressing the question through a large-scale qualitative study in Ghana. The study comprises 69 interviews across micro-enterprises, government actors, and technology firms. Building on and complementing existing research on bricolage and digital value creation, our findings motivate a new theory of digital bricolage as distinct from entrepreneurial and IT bricolage. We identify three digitalization pathways: parallel bricolage, selective bricolage, and digital planning. Together these capture a spectrum from an emergent, resource-constrained (parallel and selective digital bricolage) to a more strategic, planned (digital planning) approach to digitalization. Our findings challenge the assumption that digital resources inherently enable limitless recombination and boundless value creation. They show that digitalization through digital bricolage can have both enabling and limiting impacts. While digital bricolage fosters micro-enterprises’ short-term innovation, survival, and adaptations to resource-constraints, overreliance on this digitalization path can paradoxically constrain long-term value creation due to limited functionality, integration issues, and reliance on the bricoleur’s personal capabilities. This leads to a digital bricolage trap, where accumulated compromises lock enterprises into fragmented, low-capability digital states. We offer an alternative perspective to traditional digitalization theory, which assumes access to mature digital infrastructures, advanced technologies and straightforward value generation. Our findings better account for the digitalization of smaller enterprises as a process of customizing affordable digital tools in ways that reflect local creativity and constraints
Digital platformisation as public sector transformation strategy: a case of Ghana’s paperless port
Full text linkPublic sector organisations around the world are deploying digital platforms as part of their transformational strategy. However, prior research has predominantly focused on developed economies with stable institutional environments, while limited studies exist on less developed economies. Notwithstanding the digital divide, institutional voids, economic and development challenges facing less developed economies, digital platformisation as a strategy is fuelling technology leapfrogging in public sector transformation. Drawing on a case study of Ghana’s paperless port digital transformation and the technology affordance theory, we address the research question: “How can digital platformisation facilitate public sector transformation?” Based on the findings and the technology affordance theory, this study develops a transformational affordance framework (TAF) and offers propositions on how digital platforms can enable public sector transformation
Forecasting digital asset return : an application of machine learning model
No full textIn this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q-learning, XGBoost and ARFIMA-GARCH. The findings show that the double deep Q-learning model outperforms the others in terms of returns and Sortino ratio and is capable of one-step-ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model
An early-warning risk signals framework to capture systematic risk in financial markets
No full textearly-warning risk signals
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Abstract
Despite extensive research on the relationship between systematic risk and expected returns, there exists limited knowledge of how early-warning risk signals could capture investors’ expectations about changes in systematic risk. Leveraging on graph theory and covariance matrices, this study proposes a novel framework to develop risk signal patterns. Our approach not only discerns high-risk periods from calmer ones but also elucidates the pivotal role of interconnections among securities as indicators of systematic risk. The findings offer actionable insights for timely portfolio management and risk management responses in periods of transitions towards higher systematic risk. Moreover, by leveraging on graph theory, regulators can take timely decisions about how much liquidity to inject into the markets during periods of uncertainty. This study contributes to the literature by establishing a novel framework on linking investors’ expectations and expected changes in systematic risk
Technology transfer potential in local and foreign-owned firms in emerging economies
No full textTechnology transfer in international collaborations is challenging but can bring benefits to both local and foreign-owned firms in emerging economies. In this article we focus on conditions for potential technology transfer in emerging economies. We develop a configurational theoretical framework and empirically operationalise it using qualitative comparative analysis (QCA). Building on differences in absorptive capacity between these two kinds of firms and relying on data from the construction industry in Ghana, we develop a process model of technology transfer in emerging economies. Our model shows that technology transfer in local and foreign firms can be achieved through different combinations of human resource development (HRD) and knowledge management (KM) as well as international collaborations and networks. The model also explicates mechanisms leading to potential technology transfer. Based on the findings and the process model, the study makes several contributions to the absorptive capacity and technology transfer literature in emerging economies by shedding light on the underlying processes that foster a firm’s ability to absorb technology in international collaborations
Understanding fraudulent returns and mitigation strategies in multichannel retailing
Full text linkThe growth of online retailing has exceeded expectations over the last few years. This has resulted in high product return rates, which retailers are struggling with due to complex and costly returns processing, logistics, and financial implications. Additionally, online returns come with increased opportunities for returns fraud. During the pandemic, new types of returns fraud have emerged and returns fraud rates have increased across all channels. Based on a series of semi-structured interviews with retailers and retail experts, we investigate factors that enable fraudulent returns from consumers' and retailers’ perspectives and outline strategies for retailers to combat product returns fraud in a multichannel environment, leading to a framework for retail fraud. We contribute critical insights to research and practices on understanding and addressing a growing problem that has economic, social and environmental implications.</p
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